Abstract
Recurrent neural networks of the Reservoir Computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of temporal memory. Here with the aim to obtain extended temporal memory in generic delayed response tasks, we combine a generalised intrinsic plasticity mechanism with an information storage based neuron leak adaptation rule in a self-organised manner. This results in adaptation of neuron local memory in terms of leakage along with inherent homeostatic stability. Experimental results on two benchmark tasks confirm the extended performance of this system as compared to a static RC and RC with only intrinsic plasticity. Furthermore, we demonstrate the ability of the system to solve long temporal memory tasks via a simulated T-shaped maze navigation scenario.
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© 2012 Springer-Verlag Berlin Heidelberg
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Dasgupta, S., Wörgötter, F., Manoonpong, P. (2012). Information Theoretic Self-organised Adaptation in Reservoirs for Temporal Memory Tasks. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_4
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DOI: https://doi.org/10.1007/978-3-642-32909-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
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